Skip to main content

Authors in a Markov matrix: Which author do people find most inspiring? (15)


Eigenanalysis

At which station am I?

An adjacency matrix represents graph topology (how the nodes are connected). However, a matrix can not only represent the connections, but also can be applied to a vector and can generate a new vector. We saw the adjacency matrix can generate a new station vector. We can continue this computation a bit more. Why do we this? I want to show you a bit interesting stuffs. Let's assume we are first at Weinmeisterstr. We repeat visiting to next station or staying the station. Each computation result shows how many possible paths are there to reach each station. Let's see how this number goes.

This is initial state, we are at Weinmeisterstr station.
\begin{eqnarray*} \begin{array}{|c|c|} \hline \mbox{Station} & \mbox{1 step} \\ \hline \mbox{Weinmeisterstr} & 1 \\ \mbox{Alexanderplatz} & 0 \\ \mbox{Hackescher Markt} & 0 \\ \mbox{Jannowitzbruecke} & 0 \\ \hline \end{array} \end{eqnarray*} Second step result is:

\begin{eqnarray*} \begin{array}{|c|c|} \hline \mbox{Station} & \mbox{2 steps} \\ \hline \mbox{Weinmeisterstr} & 2 \\ \mbox{Alexanderplatz} & 2 \\ \mbox{Hackescher Markt} & 1 \\ \mbox{Jannowitzbruecke} & 1 \\ \hline \end{array} \end{eqnarray*}

Let's continue to 3 steps, 5 steps, and 10 steps.
\begin{eqnarray*} \begin{array}{|c|c|c|c|} \hline \mbox{Station} & \mbox{3 steps} & \mbox{4 steps} & \mbox{10 steps} \\ \hline \mbox{Wein.} & 4 & 26 & 3862 \\ \mbox{Alex.} & 6 & 44 & 6688 \\ \mbox{Hack.} & 3 & 25 & 3861 \\ \mbox{Jann.} & 3 & 25 & 3861 \\ \hline \end{array} \end{eqnarray*}
I guess the number of paths to be Alexanderplatz seems twice larger than to be other stations. I think this kind of guess is important in mathematics. I enjoy mathematics by finding a pattern.

Actually, it is not a coincidence that the possible number of paths to go to Alexanderplatz is twice to the other stations when number steps is large. It is interesting to me that such a pattern is here, this would have been more chaotic. The following sections, I would like to look into more details. However, I hope you have some kind of feeling that we can analyze the relationship using graph theory and adjacency matrix.

I would like to come back to the first question. That is ``How can we analyze the relationship of authors''. The mathematics we see here consider only relationship between objects. We see the examples of numbers, authors, and train stations. But graph theory and adjacency matrix don't care what are the objects. Figures 4, 5, 6 are all the same graph and shares the same adjacency matrix. You might think this is careless or inhuman. But on the other hand, don't care means anything can fit this. We can use these tools (graph theory and adjacency matrix) not only for train stations, but also for authors. This makes these tools powerful and versatile. We see we can compute how to visit the train stations like from station A to station B. We can also compute how to reach the person like from person A to person B. We can find a pattern in relationships.
Figure 4:Edges connect nodes.
Figure 5. The same graphs. Graph only cares the connections between nodes.
Figure 6: Graph example 1. Each node is an English author.
In the next article, we will see a method of finding ``a feature of adjacency matrix''.

Comments

Popular posts from this blog

Why parallelogram area is |ad-bc|?

Here is my question. The area of parallelogram is the difference of these two rectangles (red rectangle - blue rectangle). This is not intuitive for me. If you also think it is not so intuitive, you might interested in my slides. I try to explain this for hight school students. Slides:  A bit intuitive (for me) explanation of area of parallelogram  (to my site, external link) . 

Geometric Multiplicity: eignvectors (2)

If eigenvectors of a matrix A are independent, it is a happy property. Because the matrix A can be diagonalized with a matrix S that column vectors are eigenvectors of A . For example, Why this is a happy property of A? Because I can find A's power easily. A^{10} is not a big deal. Because Λ is a diagonal matrix and power of a diagonal matrix is quite simple. A^{10} = SΛ^{10} S^{-1} Then, why if I want to compute power of A ? That is the same reason to find eigenvectors. Eigenvectors are a basis of a matrix. A matrix can be represented by a single scalar. I repeat this again. This is the happy point, a matrix becomes a scalar. What can be simpler than a scalar value. But, this is only possible when the matrix S's columns are independent. Because S^{-1} must be exist. Now I come back to my first question. Is the λ's multiplicity related with the number of eigenvectors? This time I found this has the name. Geometric multiplicity (GM): the number of in...

Tezuka Osamu's Black Jack, "Shrinking"

I like several novel authors. My first favorite author is probably Teduka, Osamu. I still love him. The list grows by adding Hoshi, Shinichi, Agatha Christie, Hermann Hesse, and so forth. My first favorite article of Tezuka was Atom as most of the (boy's) Tezuka fans did. But my favorite is Black Jack. I try to summarize one story, it is still quite vivid in my memory. I first read this story when I was 13 - 15 years old. I re-read it at least several times since Black Jack is composed of many short episodes. The title should be "ちぢむ (SHRINKING)" or it might be "縮む(Shrinking)". (It is not so convenient to translate this to English, since English does not have a system to say the exact same word in several ways. So I just simulate it with capital letters.) Black Jack is a genius surgeon, but he does not have the license. In short, his medical activity is illegal. His skill is top level in the world, but, the fee is also out-of-law expensive. In the story ...